Artificial Intelligence (AI) has become commonplace to solve routine everyday tasks. Because of the exponential growth in medical imaging data volume and complexity, the workload on radiologists is steadily increasing. We project that the gap between the number of imaging exams and the number of expert radiologist readers required to cover this increase will continue to expand, consequently introducing a demand for AI-based tools that improve the efficiency with which radiologists can comfortably interpret these exams. AI has been shown to improve efficiency in medical-image generation, processing, and interpretation, and a variety of such AI models have been developed across research labs worldwide. However, very few of these, if any, find their way into routine clinical use, a discrepancy that reflects the divide between AI research and successful AI translation. To address the barrier to clinical deployment, we have formed MONAI Consortium, an open-source community which is building standards for AI deployment in healthcare institutions, and developing tools and infrastructure to facilitate their implementation. This report represents several years of weekly discussions and hands-on problem solving experience by groups of industry experts and clinicians in the MONAI Consortium. We identify barriers between AI-model development in research labs and subsequent clinical deployment and propose solutions. Our report provides guidance on processes which take an imaging AI model from development to clinical implementation in a healthcare institution. We discuss various AI integration points in a clinical Radiology workflow. We also present a taxonomy of Radiology AI use-cases. Through this report, we intend to educate the stakeholders in healthcare and AI (AI researchers, radiologists, imaging informaticists, and regulators) about cross-disciplinary challenges and possible solutions.

翻译：人工智能（AI）已成为解决常规日常任务的常见技术。由于医学影像数据量和复杂性的指数级增长，放射科医生的工作负荷正稳步增加。我们预计，影像检查数量与覆盖这一增长所需的放射科专家解读人数之间的差距将持续扩大，因此亟需基于AI的工具来提高放射科医生舒适解读这些检查的效率。AI已被证明在医学图像生成、处理和解读中能提升效率，全球各研究实验室已开发出多种此类AI模型。然而，这些模型极少（如果有的话）进入常规临床使用，这一反差反映了AI研究与成功AI转化之间的鸿沟。为应对临床部署的障碍，我们成立了MONAI联盟——一个开源社区，致力于制定医疗机构中AI部署的标准，并开发工具和基础设施以促进其实施。本报告汇集了MONAI联盟中行业专家和临床医生群体数年来的每周讨论及实践问题解决经验。我们识别了从研究实验室的AI模型开发到后续临床部署之间的障碍，并提出了解决方案。我们的报告为将影像AI模型从开发阶段推进至医疗机构临床实施的过程提供了指导。我们讨论了临床放射学工作流程中各种AI集成点，并提出了放射学AI用例的分类法。通过本报告，我们旨在向医疗和AI领域的利益相关者（AI研究人员、放射科医生、影像信息学专家及监管机构）普及跨学科挑战及可能的解决方案。